Title :
Possibilistic-Scenario Model for DG Impact Assessment on Distribution Networks in an Uncertain Environment
Author :
Soroudi, Alireza
Author_Institution :
Sci. & Res. Branch, Islamic Azad Univ., Tehran, Iran
Abstract :
The distribution network operators (DNOs) are responsible for securing a diverse and viable energy supply for their customers so the technical and economical impacts of distributed generation (DG) units are of great concerns. Traditionally, the DNOs try to maximize the technical performance of the distribution network, but it is evident that the first step in optimizing a quantity is being able to calculate it. The DG investment/operation which is performed by distributed generation operators/owners (DGOs) (under unbundling rules) has made this task more complicated. This is mainly because the DNO is faced with the uncertainties related to the decisions of DG investors/operators where some of them can be probabilistically modeled while the others are possibilistically treated. This paper proposes a hybrid possibilistic-probabilistic DG impact assessment tool which takes into account the uncertainties associated with investment and operation of renewable and conventional DG units on distribution networks. This tool would be useful for DNOs to deal with the uncertainties which some of them can be modeled probabilistically and some of them are described possibilistically. The proposed method has been tested on a test system and a large-scale real distribution network to demonstrate its strength and flexibility.
Keywords :
distributed power generation; distribution networks; power system security; power system simulation; distributed generation impact assessment; distributed generation operators/owners; distributed generation units; distribution network operators; distribution networks; energy supply; possibilistic-scenario model; Investments; Load modeling; Probabilistic logic; Probability density function; Uncertainty; Wind speed; Wind turbines; Distributed generation (DG); fuzzy sets; risk analysis; stochastic approximation; uncertainty; wind energy;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2011.2180933